“What is the next ‘best’ product or service we should be offering each of our customers?” This is a challenging question, but the good news is that, if you’re asking it, you’re already pointed in the right direction.
It’s typical for banks to be organized around product groups, so the more common question they ask is, “Who can I try to sell a CD (or Credit Card or HELOC) to?” There are a couple of key problems with this approach. First, this product-centered approach often leads to uncoordinated, inefficient communications with customers. Second, it doesn’t consider what the individual customer really needs or wants. The net result is a confusing or disappointing customer experience, with customers less likely to respond positively.
The “Next Best Offer” strategy, on the other hand, starts with knowledge about the individual customer so that you know which product or service is the right thing to offer that customer. Done well, this is a win-win approach. Customers get the financial products and services they want, and the bank develops a deeper, more valuable relationship with the customer.
There are four key steps to developing an effective Next Best Offer strategy:
- Develop an integrated customer view
Big Data may sound impersonal or even scary, but good information about your customers is foundational to providing relevant targeted offers. Gather together everything you know about your customers:
- Current/previous products and services – e.g., open checking and credit card accounts, direct deposit, closed HELOC
- Balance and transactional history – e.g., checking balance has exceeded $10K for first time; one recurring monthly payment; mortgage payment from checking account to another financial institution
- Demographic data (self-reported or 3rd party) – e.g., age, income, number/age of kids
- Channel preferences and usage – e.g., customer rarely visits branch, online banking, receives both mail and email statements/communications, customer uses mobile app
Don’t worry if you don’t have all the information you want. Start with what you have and add more detail later.
- Build next best offer algorithm
This can be done in many ways, ranging from a handful of simple business rules to a multi-layered combination of predictive models. Typically, the goal of the algorithm is to maximize expected value from the customer, which combines the likelihood to accept the offer with the value (to the bank) if the customer does accept. Your analytic approach should depend on a few factors:
- How sophisticated is your current approach to cross-sell? You need to walk before you run. Don’t try to overcomplicate things if a simple decision tree will be a big improvement
- How much data do you have? A sophisticated model will not be valuable if you don’t have the customer data to drive it
- What are your operational capabilities for delivering varied offers? Even with the best data and model, a Next Best Offer strategy won’t work if you can’t execute
- Execute: deliver the offers
It’s critical to think through the operational, technical and organizational implications for delivering targeted offers to each customer across channels. You need to provide a coherent experience for the customer who, for example, receives an offer in the mail and then visits a branch, or phones the call center for more information.
- Measure results
The first layer of analysis should look at number/type of offers and acceptance rate. You’ll then want to peel the onion to understand differences by customer segment and channel. Ultimately, you’ll want to see how the cross-sell efforts impact long-term customer loyalty and value to the bank. The results you get will guide you toward your next steps: What additional information do you need about each customer? How should you refine the Next Best Offer algorithm to better match customer needs? How can operations be improved or more fully leveraged to enable results? Next Best Offer is not a set-it-and-forget-it strategy. As customer needs and the bank’s priorities change, it will be important for your cross-sell strategy to evolve too, so continuous measurement and refinement are critical.
William has earned an MBA from The University of Chicago Booth School of Business, an MS in statistics from Kansas State University, and an MS in applied mathematics from Southeast University. William and his team provide data and analytic leadership to Catalyst’s clients.